Statistical Learning

Postgraduate course

Course description

Objectives and Content

Topics treated in this course include regression, classification, model selection and a certain introduction to machine learning. The student will apply different packages in R.

Learning Outcomes

After completing the course, students should be able to:

  • use nonlinear regression methods such as Spline, Local Regression and Generalized Additive Models
    apply classification methods such as Logistic Regression, Linear Discriminant Analysis
  • know how to use resampling (cross validation, bootstrap) and Model Selection methods to assess and select models and deal with high dimensional data
  • apply Tree-Based Methods such as decision tree, Bagging, Random Forests, Boosting
  • avail Support Vector Machines for resolving classification and regression problem.
  • know unsupervised learning methods such as Principal Components Analysis and Clustering Methods.
  • know about Deep learning and Naive Bayes.

Semester of Instruction

Autumn irregular, course will be offered if it is on this course list: Workbook: Emneliste for innreisende utvekslingsstudenter (

Recommended Previous Knowledge
Compulsory Assignments and Attendance
Two approved compulsory excercises
Forms of Assessment
Oral examinations. Approved compulsory exercises is required to take the exam .
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.